GPUs dominate AI, but for inference, specialized hardware like FPGAs and TPUs can offer significant advantages in latency, cost, and power efficiency for specific workloads.
Building reliable distributed systems means handling failures while keeping data consistent. Raft consensus helps manage this complexity through a clear approach to agreement.
Edge AI models aren't static. Learn why Over-the-Air updates are critical for maintaining their performance and how they differ from typical software updates.
Serving large language models to multiple users efficiently introduces significant challenges around isolation, performance, and resource management. This article explores practical strategies to handle these complexities.
Time-series databases are purpose-built for the massive amounts of time-stamped data generated by modern systems, offering unique advantages for observability and analytics.
Centralized data platforms often bottleneck AI development. Data Mesh offers a decentralized approach, empowering domain teams to own and govern their AI data products.
Deploying new AI models can be risky. Canary deployments offer a safer, staged approach to roll out models in production, minimizing impact from unexpected issues.